Neural networks for signal processing applications: ECG classification

QRS complexes were classified using a Multi Layer Perceptron (MLP) using a modified version of the original Backpropagation (BP) algorithm. The input to the network were bitmaps of the QRS complexes represented in the form of a 20 x 20 matrix. A number of hidden layers (and neurons in each hidden layer) were experimented upon to observe the rate at which the network converged. Larger Networks were observed to find the minimum on the error curve with ease. Increasing the network size beyond a certain size did not improve the performance rate, rather it decreased the performance rate. It is evident that there exists an optimal neural network architecture for every given problem. The weights change rules in Backpropagation algorithm were modified to include a variation of the relationship between momentum and learning rate to observe any increase in network's performance rate. A learning rate adaptation factor was introduced into the learning algorithm to decrease the network's chances of missing a minimum on the error curve. The network was found to perform extremely well with the modified version of the algorithm. The network converged after only 9000 learning cycles when compared to 14,000 cycles with the original algorithm.